可穿戴心电信号监测中运动伪影消除技术研究

心电图(ECG)是心脏疾病诊断最有效的工具。噪声的去除和Q波、R波、S波的提取是心电信号检测中的两大主题。本文使用Savitzky-Golay滤波器对人体在弯腰、走路、坐下-站起等运动状态下采集的心电信号进行分析,去除信号中的基线漂移和运动伪影,并对滤波后信号的Q波、R波和S波进行检测。通过将本文提出的滤波方式与卡尔曼滤波、小波分解就时间复杂度和功率谱密度两个参数进行对比分析,评估Savitzky-Golay滤波器在心电信号中运动伪影去除的优势。实验结果表明,Savitzky-Golay滤波器能更加有效地适应心电信号的变化,有效地去除心电信号中的噪声,并且最大限度保持心电波形的形状和波峰。      

Genetic algorithm and wavelet hybrid scheme for ECG signal denoising

This paper introduces an effective hybrid scheme for the denoising of electrocardiogram (ECG) signals corrupted by non-stationary noises using genetic algorithm (GA) and wavelet transform (WT). We first applied a wavelet denoising in noise reduction of multi-channel high resolution ECG signals. In particular, the influence of the selection of wavelet function and the choice of decomposition level on efficiency of denoising process was considered. Selection of a suitable wavelet denoising parameters is critical for the success of ECG signal filtration in wavelet domain. Therefore, in our noise elimination method the genetic algorithm has been used to select the optimal wavelet denoising parameters which lead to maximize the filtration performance. The efficiency performance of our scheme is evaluated using percentage root mean square difference (PRD) and signal to noise ratio (SNR). The experimental results show that the introduced hybrid scheme using GA has obtain better performance than the other reported wavelet thresholding algorithms as well as the quality of the denoising ECG signal is more suitable for the clinical diagnosis.     

Wavelet and wavelet packet compression of electrocardiograms

Wavelets and wavelet packets have recently emerged as powerful tools for signal compression. Wavelet and wavelet packet-based compression algorithms based on embedded zerotree wavelet (EZW) coding are developed for electrocardiogram (ECG) signals, and eight different wavelets are evaluated for their ability to compress Holter ECG data. Pilot data from a blind evaluation of compressed ECG's by cardiologists suggest that the clinically useful information present in original ECG signals is preserved by 8:1 compression, and in most cases 16:1 compressed ECG's are clinically useful     

Analysis of ECG from pole-zero models

A complete solution to the fundamental problem of ECG analysis, viz., delineation of the signal into its component waves, is proposed from a system theoretic point of view. The discrete cosine transform of a bell shaped biphasic function is approximated mathematically by a system function with two poles and two zeros, i.e., of order (2, 2). Using this concept as the basis, a pole-zero model of suitable order is derived from the discrete cosine transform (DCT) of the given signal using Steiglitz-McBride method. This model is expanded into a unique set of partial fractions each of order (2, 2), and a biphasic function is recovered from each one of these fractions in the inverse process. Each of the P and T waves usually requires only one biphasic function, while the QRS complex needs two or at most three such fractions. A one-to-one relationship between the pole pattern in the z-plane and component wave pattern in the time signal is established. Results of analysis of continuous strips of ECG show that the delineated component waves are in excellent agreement with the original waves both qualitatively and quantitatively. The method is robust for the analysis of signals with artifacts of various kinds, independent of the sampling rate used, and is free from ad hoc back and forth search procedures.     

Wearable Organic Electrochemical Transistor Array for Skin-Surface Electrocardiogram Mapping Above a Human Heart

Electrocardiogram (ECG) mapping can provide vital information in sports training and cardiac disease diagnosis. However, most electronic devices for monitoring ECG signals need to use multiple long wires, which limit their wearability and conformability in practical applications, while wearable ECG mapping based on integrated sensor arrays has been rarely reported. Herein, ultra-flexible organic electrochemical transistor (OECT) arrays used for wearable ECG mapping on the skin surface above a human heart are presented. QRS complexes of ECG signals at different recording distances and directions relative to the heart are obtained. Furthermore, the ECG signals are successfully analyzed by the devices before and after exercise, indicating potential applications in some sports training and fitness scenarios. The OECT arrays that can conveniently monitor spacial ECG signals in the heart region may find niche applications in wearable electronics and healthcare products in the future.     

基于Mallat算法的小波分解重构的心电信号处理

为了实现对微弱低信噪比的心电信号的有效提取,采用了Mallat算法的小波分解重构法去除心电信号的噪声。首先确定小波分解重构的小波基;其次确定分解的层数;然后直接提取有用信号所在的频带（有用信号占优的频带）进行重构;最后,Matlab仿真MIT-BIT标准数据库中的心电信号表明小波分解重构法可以有效的去除心电信号中的多种干扰;同时比起传统滤波器法来说,小波分解与重构去噪法应用起来更方便。     

基于小波变换域的参数化双谱心音信号分析方法的研究

本文利用心音信号在小波域的分布特点,提出了一种基于小波变换域的参数化双谱心音信号分析方法。通过对不同类别的心音信号数据分析表明,本方法不仅能抑制各类噪声的干扰,而且能有效地区分不同情绪状态下心音信号的模式特征,为分析人类情绪变化的特点提供了一种实用而有效的方法。     

Wavelet packets feasibility study for the design of an ECG compressor

Most of the recent electrocardiogram (ECG) compression approaches developed with the wavelet transform are implemented using the discrete wavelet transform. Conversely, wavelet packets (WP) are not extensively used, although they are an adaptive decomposition for representing signals. In this paper, we present a thresholding-based method to encode ECG signals using WP. The design of the compressor has been carried out according to two main goals: (1) The scheme should be simple to allow real-time implementation; (2) quality, i.e., the reconstructed signal should be as similar as possible to the original signal. The proposed scheme is versatile as far as neither QRS detection nor a priori signal information is required. As such, it can thus be applied to any ECG. Results show that WP perform efficiently and can now be considered as an alternative in ECG compression applications.     

应用最优小波包变换的特征提取方法

在模式识别或分类中,从原始模式中提取有效的分类特征是非常重要的.但对于大量的非平稳或时变信号模式来说,如语音,雷达,地震信号等,用于分类的特征往往包含在局部的时-频信息中,用一般的变换方法提取有效的特征比较困难.近年来小波变换在信号处理和特征提取中得到了广泛应用,但小波包变换的任意多尺度分解特性,是分析非平稳信号更有效的方法,这是由于小波库中包含了丰富的小波包基,不同的小波包基具有不同的性质,反映不同的信号特性,能获取其他变换所不能获取的信号特征.本文主要研究由给定的训练样本集,如何选择最优小波包基,从被识别或分类的信号中提取具有最大可分性的特征.为此提出了应用三种可分性准则,即距离准则,散度准则和熵准则选择最优基.通过实验,对应用各准则选择最优基提取特征与小波基提取特征的性能进行了比较.     

应用小波变换去除膈肌肌电图信号中的心电干扰

膈肌肌电图信号是微弱的人体生物电信号,该信号往往受到被测对象自身心电图信号的严重干扰.本文应用小波变换的分析方法,提出了一种心电定位和小波阈值相结合的去心电新算法.该方法在对信号各层小波系数分析的基础上,通过小波相关系数法确定心电干扰的位置和范围,并用绝对值均值的阈值算法对该范围内的小波系数进行阈值处理.实验结果表明,该方法能够有效去除膈肌肌电图信号中的心电干扰,并较好地保留了膈肌肌电图信号的信号特征,为膈肌肌电图信号用于呼吸疾病的分析诊断创造良好的条件.     

The homomorphic analytic signal

This paper introduces an alternative form of analytic signal that is formed using homomorphic signal processing techniques. It may be generated for signals that demonstrate redundancy in their pole-zero distributions, such as phase signals and real signals. The analytic signal is formed by manipulating the positions of the signal's nonminimum phase poles and zeros to create a minimum phase signal. A factorization of real signals is presented that demonstrates the properties of the homomorphic analytic signal, and a new definition of instantaneous frequency is developed. Examples are given to verify the theory and comparisons with the linear analytic signal, and instantaneous frequency are made     

Model-based imaging of cardiac electrical excitation in humans

Activation time (AT) imaging from electrocardiographic (ECG) mapping data has been developing for several years. By coupling ECG mapping and three-dimensional (3-D) + time anatomical data, the electrical excitation sequence can be imaged completely noninvasively in the human heart. In this paper, a bidomain theory-based surface heart model AT imaging approach was applied to single-beat data of atrial and ventricular depolarization in two patients with structurally normal hearts. In both patients, the AT map was reconstructed from sinus and paced rhythm data. Pacing sites were the apex of the right ventricle and the coronary sinus (CS) ostium. For CS pacing, the reconstructed AT pattern on the endocardium of the right atrium was compared with the CARTO map in both patients. The localization errors of the origins of the initial endocardial breakthroughs were determined to be 6 and 12 mm. The sites of early activation and the areas with late activation were estimated with sufficient accuracy. The reconstructed sinus rhythm sequence was in good qualitative agreement with the pattern previously published for the isolated Langendorff-perfused human heart.     

Multiple Multidimensional Morse Wavelets

This paper defines a set of operators that localize a radial image in space and radial frequency simultaneously. The eigenfunctions of the operator are determined and a nonseparable orthogonal set of radial wavelet functions are found. The eigenfunctions are optimally concentrated over a given region of radial space and scale space, defined via a triplet of parameters. Analytic forms for the energy concentration of the functions over the region are given. The radial function localization operator can be generalised to an operator localizing any L²(Ropf²) function. It is demonstrated that the latter operator, given an appropriate choice of localization region, approximately has the same radial eigenfunctions as the radial operator. Based on a given radial wavelet function a quaternionic wavelet is defined that can extract the local orientation of discontinuous signals as well as amplitude, orientation and phase structure of locally oscillatory signals. The full set of quaternionic wavelet functions are component by component orthogonal; their statistical properties are tractable, and forms for the variability of the estimators of the local phase and orientation are given, as well as the local energy of the image. By averaging estimators across wavelets, a substantial reduction in the variance is achieved     

Harmonic Analysis of SAW Transducers

The excitation function of an interdigital transducer (IDT) is determined by measuring the discrete impulse response, taking into account the first seven harmonics of the frequency domain. Using a time segregation method, all non-SAW time-domain components are suppressed. A single transducer is isolated by a method of autodeconvolution that utilizes a theoreticalIy derived phase function. The resulting excitation function provides experimental insight into the operation of IDT electrodes and compares well with the theoretical response of Smith and Pedler. The basic analysis technique can be used for other configurations, once the frequency-domain phase response of one transducer is known.     

Analyzing high-density ECG signals using ICA

The analysis of ECG signals is of fundamental importance for cardiac diagnosis. Conventional ECG recordings, however, use a limited number of channels (12) and each records a mixture of activities generated in different parts of the heart. Therefore, direct observation of the ECG signals collected on the body surface is likely an inefficient way to study and diagnose cardiac abnormalities. This study describes new experimental and analytical methods to capture more meaningful ECG component signals, each representing more directly a physical cardiac source. This study first describes a simply applied method for collecting high-density ECG signals. The recorded signals are then separated by independent component analysis (ICA) to obtain spatially fixed and temporally independent component activations. Results from five subjects show that P-, QRS-, and T-waves can be clearly separated from the recordings, suggesting ICA might be an effective and useful tool for high-density ECG analysis, interpretation, and diagnosis.      

Study of features based on nonlinear dynamical modeling in ECG arrhythmia detection and classification

We present a study of the nonlinear dynamics of electrocardiogram (ECG) signals for arrhythmia characterization. The correlation dimension and largest Lyapunov exponent are used to model the chaotic nature of five different classes of ECG signals. The model parameters are evaluated for a large number of real ECG signals within each class and the results are reported. The presented algorithms allow automatic calculation of the features. The statistical analysis of the calculated features indicates that they differ significantly between normal heart rhythm and the different arrhythmia types and, hence, can be rather useful in ECG arrhythmia detection. On the other hand, the results indicate that the discrimination between different arrhythmia types is difficult using such features. The results of this work are supported by statistical analysis that provides a clear outline for the potential uses and limitations of these features.     

小波变换在心电信号处理中的应用

在心脏病诊断过程中,心电信号的检测是重要的环节,然而心电信号的噪声很强,为了能够较好地滤除信号中的噪声,对信号的特点进行准确标定,利用基于小波变换的阈值去噪算法和基于小波的模极大值-极小值的算法进行心电信号的处理.采用MIT/BIH中的数据进行仿真调试验证,实验结果表明,被引入的几种噪声能被很好地去除,而且心电信号能较完整地保留下来,特征点能被准确地检测到,从而提高了诊断心脏等疾病的诊断效率.     

激光导波阵列波束成形损伤成像方法

激光超声检测方法在结构损伤检测领域有着广泛应 用。增加激光信号用于损伤成像的技术难点。针对此挑战,采用提出的Morlet小波分析法提 取激光宽频信号中对应中心频率下的窄带信息。为实现损伤 的可视化,分析比较了导波阵列波束成形损伤成像算法中时间延迟叠加算法和相位延迟叠加 算法,其中相位延迟叠加成像法对频域内所有频率成分实施延迟叠加能克服导波的频散效应 得影响,能实现对损伤更精确的定位。为验证该方法的实用性,构建了激光激励/接收完全 非接触式的实验系统,通过对Morlet小波分析后的信号采用延迟叠加算法对损伤进行成像, 实验结果表明了:相位延迟叠加成像法成像效果比时间延迟成像法成像效果更佳,定位更精 确。     

基于改进差分阈值算法的心电检测技术研究

针对差分阈值算法中固定阈值的局限性,文中提出了一种基于自适应波峰阈值和R波间隔阈值的算法。该算法结合心电信号特点自动选择波峰阈值,并选择R波间隔阈值,提高了算法的自适应性和准确率。文中以MIT-BIH心律失常数据库中的心电信号作为实验样本,采用带通滤波与小波阈值滤波相结合的方法完成心电信号去噪,采用改进差分自适应阈值算法对心电信号进行波形检测。实验结果表明,该算法能够将心电信号R波的检测准确率提升到99.57%,有效减少了误检、漏检问题的发生,并可准确完成心率、心率变异性、身体疲劳度、精神疲劳度计算和常见心律失常分类。     

Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings

Obstructive sleep apnea syndrome (OSAS) is associated with cardiovascular morbidity as well as excessive daytime sleepiness and poor quality of life. In this study, we apply a machine learning technique [support vector machines (SVMs)] for automated recognition of OSAS types from their nocturnal ECG recordings. A total of 125 sets of nocturnal ECG recordings acquired from normal subjects (OSAS- ) and subjects with OSAS (OSAS+), each of approximately 8 h in duration, were analyzed. Features extracted from successive wavelet coefficient levels after wavelet decomposition of signals due to heart rate variability (HRV) from RR intervals and ECG-derived respiration (EDR) from R waves of QRS amplitudes were used as inputs to the SVMs to recognize OSAS +/- subjects. Using leave-one-out technique, the maximum accuracy of classification for 83 training sets was found to be 100% for SVMs using a subset of selected combination of HRV and EDR features. Independent test results on 42 subjects showed that it correctly recognized 24 out of 26 OSAS + subjects and 15 out of 16 OSAS - subjects (accuracy = 92.85%; Cohen's kappa value of 0.85). For estimating the relative severity of OSAS, the posterior probabilities of SVM outputs were calculated and compared with respective apnea/hypopnea index. These results suggest superior performance of SVMs in OSAS recognition supported by wavelet-based features of ECG. The results demonstrate considerable potential in applying SVMs in an ECG-based screening device that can aid a sleep specialist in the initial assessment of patients with suspected OSAS.